Momentum Term Based Blind Source Separation Algorithm and Its Performance Modified Strategies
Geng Cha · Dianzi xuebao · 2014
Momentum term technology is an effective solution to improve the performance of the adaptive blind source separation(BSS)algorithm,but the convergence property of the momentum term based BSS algorithm is very sensitive to the fixed momentum factor,and its performance in steady state is also restricted by the step size. Firstly,the principle of the momentum term based BSS algorithm as well as its two disadvantages were presented and analyzed in this paper. Then,in order to eliminate the first disadvantage of the momentum term based algorithm using the fixed momentum factor,we structured a variable momentum factor algorithm with the adaptive adjustment property based on the gradient descentmethod.On this basis,by virtue of the convex combination theory,an adaptive combination of tow variable momentum factor algorithms with differentstepsize was proposed to alleviate the performance restriction caused by the stepsize. The simulation results in different conditions demonstrate that the proposed modified strategies got the optimization balance between the fast convergence speed and small steady-state error,and effectively avoid the two drawbacks of the momentum term based BSS algorithm.